In the past, the searching of images has been performed by searching text information associated with a particular image. Examples of text information associated with images include date, photographer, subject, view, time, etc. When searching for an image of oneself as a child, one searches images associated with keywords indicative of oneself from dates corresponding with one's childhood. Text based searching, or keyword searching is a very common searching technique for information, images, data, etc.
Recently, it has been found that keywords inadequately define images. Keywords partially define search criteria, often insufficiently. For keywords to fully define search parameters, a very large number of keywords is necessarily associated with each image or photograph. Returning to the above example, when searching for an image of oneself as a child in a particular room, with a particular toy, wearing a particular outfit, and where certain pieces of furniture are visible, it is unlikely that keywords pertaining to such elements are associated with any of the images. Therefore, in order to perform the search, a number of images are identified and then each image is reviewed by a person to identify the further criteria and select those images that match the criteria. For vast image databases, the above method is prohibitive.
Thus, it is evident that though keyword searching is an extremely powerful tool, when applied to images, it is limited in applicability to those elements for which keywords are associated with the images.
It is also evident that the above description is limited to situations where keywords are correctly entered. Errors in keywords result in misfiling of images.
In an attempt at overcoming these and other drawbacks in keyword searching, it has been suggested to search pictures using a picture as the search criteria. Using such a system, a search criteria image is analysed for colour, composition, contours, etc. and the results are compared against results of similar analyses performed on stored images. Images with results similar to those of the search criteria image within predetermined limits are selected as results of the search.
Searching two (2) dimensional images also has drawbacks. A bi-dimensional image does not have scale unless calibration is performed during image acquisition. Occlusion--hiding or obstruction of objects--and auto-occlusion--hiding or obstruction of portions an object by other portions of a same object--result in the loss of a significant fraction of available information. An object has a position and orientation that are easily varied and difficult to analyse. Entire images are often oriented differently, due to camera skew, complicating analysis. Most geometrical information is lost in capturing a two (2) dimensional image. This information is difficult to reconstruct absent quite a few assumptions or known parameters. Two (2) dimensional imaging is also affected by lighting. Light casts shadows that affect the perceived image. Careful colouring of an object renders it larger or shaped differently within a two (2) dimensional image. Old movie producers took advantage of this and painted sets for backdrops instead of moving film equipment to a different location. On film, it is difficult to distinguish a well painted set within a single frame, and a real "on-location" background.
Searching and locating two (2) dimensional images may have many applications. Unfortunately, it does not allow for recognition of objects or detection of similar objects within images except with regards to a particular view of the objects.